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Exploratory Data Analysis of Automobile Dataset using Python, NumPy, Pandas, Matplotlib and Seaborn .

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Exploratory-Data-Analysis-Of-Automobile-Data

Automobile Exploratory Analysis Project

For this project we will analyze the Automobile dataset. The dataset contains the following fields:

  • symboling: Represents the insurance risk rating of the car (-3 to +3).
  • normalized-losses: Represents the average loss payment per insured vehicle year.
  • make: The manufacturer or brand of the car.
  • fuel-type: The type of fuel the car uses (e.g., diesel, gas).
  • aspiration: The method by which air is delivered to the engine (e.g., standard, turbo).
  • num-of-doors: The number of doors on the car (e.g., two, four).
  • body-style: The body style of the car (e.g., sedan, hatchback).
  • drive-wheels: The type of drive wheels (e.g., front-wheel drive, rear-wheel drive).
  • engine-location: The location of the engine in the car (e.g., front, rear).
  • wheel-base: The distance between the centers of the front and rear wheels.
  • length: The length of the car.
  • width: The width of the car.
  • height: The height of the car.
  • curb-weight: The weight of the car without passengers or cargo.
  • engine-type: The type of engine (e.g., dohc, ohcv).
  • num-of-cylinders: The number of cylinders in the engine (e.g., four, six).
  • engine-size: The size of the engine in cubic centimeters (cc).
  • fuel-system: The type of fuel system (e.g., mpfi, 2bbl).
  • bore: The diameter of each cylinder in the engine.
  • stroke: The distance the piston travels in each cycle.
  • compression-ratio: The ratio of the maximum to minimum volume in the engine's combustion chamber.
  • horsepower: The power generated by the engine.
  • peak-rpm: The maximum revolutions per minute of the engine.
  • city-mpg: The miles per gallon (MPG) in city driving conditions.
  • highway-mpg: The miles per gallon (MPG) in highway driving conditions.
  • price: The price of the car (target variable for analysis).

The charts below showcase some of the data visualizations created in this project:

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Exploratory Data Analysis of Automobile Dataset using Python, NumPy, Pandas, Matplotlib and Seaborn .

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